AI Chatbot DevelopmentGROUNDED ANSWERS
Most embarrassing chatbot screenshots are not model failures. They are grounding failures. We build assistants that answer from your own documentation, say so when they don't know, and hand the conversation to a person before a customer has to ask twice.
Ships to every channel
From your own content
No confident guessing
Transcript goes with it
Against a test set
What we build
Eight kinds of work, and the last two matter more than they look: deciding whether a bot should exist at all, and testing it hard enough that its first public conversation isn't its most memorable one.
A retrieval layer over your own content, a prompt written for your product, and business logic that does something once the answer is settled. Scoped to one job first, widened when the transcripts say it is safe to.
What's involved
Assistants that sit behind your login: policy and process questions, ticket triage, order and record lookups. Retrieval follows the user's permissions, so nobody reads a document through the bot that they could not open themselves.
What's involved
Replying in the customer's language is the easy half. The risk is retrieving an English document and paraphrasing it into another language with the meaning quietly altered, so the source and the answer language stay explicit.
What's involved
Answer what is genuinely answerable — order status, returns, account questions — and get out of the way fast on everything else, passing the conversation to an agent with the full transcript attached.
What's involved
The same assistant, reached by phone. Speech in and out, endpointing and barge-in so callers can interrupt, and a transfer path to a person — all inside a latency budget a spoken conversation can survive.
What's involved
WhatsApp, Messenger, Instagram, Slack, Teams and the widget on your own site. Each has its own template rules, session windows and message limits, and the assistant has to behave inside them rather than around them.
What's involved
Before anything is built: which questions arrive often enough to be worth automating, whether your content can answer them, and which conversations should never leave a person. Sometimes the honest recommendation is to fix the help centre first.
What's involved
A graded test set built from your real questions, re-run on every prompt, model or content change. Plus the adversarial half: prompt injection, off-topic bait, and the questions a journalist would try first.
What's involved
Under the hood
Not every message should reach a model. Frequent, high-stakes questions run on fixed intents with approved answers; the long tail goes to retrieval and generation. Deciding where that line sits is most of the design work.
Replies are assembled from your documentation, product data and policies, with the source shown next to them. When retrieval finds nothing usable, the assistant says so instead of filling the gap with something plausible.
Earlier turns, the customer's order or account, and where they are in your product — carried through the session, so nobody is asked for their order number three times.
Escalation on low confidence, on sentiment, on named topics, or simply because the customer asked. The agent picks up the transcript and everything already collected; the customer does not start again.
Read from and write to CRM, helpdesk, order and payment systems: check a status, raise a ticket, apply a refund within limits you define. Every action is logged and reversible on paper.
Web, WhatsApp, Messenger, Instagram, Slack, Teams and email. Session windows, template approval and message formats differ per channel, so the assistant's behaviour has to differ with them.
Deflection and escalation rates, unanswered questions, retrieval misses, and the exact turns where people gave up. The output is a queue of things to change, not a wall of charts.
Topic boundaries, output filtering, PII redaction, and defences against content that tries to talk the assistant out of its instructions. Plus rate limiting and abuse handling, because a public bot is a public endpoint.
Where they get deployed
Take the repetitive questions off the queue, escalate the rest quickly, and keep the transcript attached to the ticket.
Qualify enquiries inside the conversation, book the call, and write the lead to the CRM with the context that produced it.
Order status, returns and exchanges, delivery questions, and product help that reads your catalogue instead of guessing at it.
Policy and benefits questions answered from the handbook, plus screening and onboarding steps that route to a person when they should.
Account and transaction questions with identity checks in front, tight topic boundaries, and a hard stop before anything becomes regulated advice.
Appointments, preparation instructions and general information — with clinical questions escalated to a person rather than answered.
First-line triage against your runbooks: reset flows, known-issue lookups, and tickets raised with the diagnostics already attached.
Design decisions come out of the conversations you already have — the questions people ask most, and the ones your help centre answers badly — rather than out of a workshop whiteboard.
Not a template with your logo on it. The retrieval layer, the prompts and the guardrails are shaped by what your documentation actually covers, and by the gaps it doesn't.
Prompt-injection testing, topic boundaries, PII redaction and encrypted storage. Where GDPR, HIPAA or PCI DSS obligations apply to you, we scope them during discovery and build the system to support them.
The people who scope it build it, evaluate it, and keep tuning it once real users arrive. Nothing is handed over a wall halfway through.
Answer quality is graded against a test set built from your own questions, so every prompt, model or content change can be compared with the last one instead of argued over.
6 tools
OpenAI GPTGPT-4 & GPT-4o
Claude AIAnthropic models
Llama ModelsOpen-source LLMs
TensorFlowDeep learning
PyTorchML research
Scikit-learnClassical ML5 tools
LangChainLLM orchestration
Dialogflow (GCP)Google NLP
Azure Bot ServiceMicrosoft5 tools
PythonCore language
Node.jsServer runtime
JavaEnterprise
PHPWeb backend
Ruby on RailsRapid dev3 tools
AWSAmazon CloudAzureMicrosoft CloudGoogle CloudGCP4 tools
MongoDBDocument DB
PostgreSQLRelational
MySQLSQL database
PineconeVector databaseSeven steps. The ones that decide whether it works are the reading at the start and the tuning at the end — the build in the middle is the predictable part.
We read your tickets, chat logs and site-search queries to see which questions arrive often enough to be worth automating — and which ones your content cannot answer today at all.
What the assistant handles, what it must refuse, what it escalates and to whom, and how success will be judged. All of it written down before anyone opens an editor.
Dialogue flows, intent coverage, tone, and the exact wording for not knowing. Fallbacks get as much attention as the happy path, because that is where a bot embarrasses you.
Ingest and chunk your content, wire the index, write the prompts, and connect the actions — order lookups, ticket creation, CRM writes — behind API boundaries you already control.
Graded runs against a test set drawn from real questions, plus prompt injection, off-topic bait and the hostile phrasing every public bot attracts. Failures become test cases rather than surprises.
Live on a single channel or a share of traffic, with escalation switched on and the fallback tuned conservatively. Widen only when the transcripts justify it.
Unanswered questions, retrieval misses and escalations get reviewed on a schedule. Content gaps go back to your team; prompt and threshold changes go through the eval suite first.
Balance and transaction questions, card blocks and disputes — with identity checks in front and a hard stop before anything becomes regulated advice.
Appointment booking, preparation instructions and general information, with anything clinical routed to a person rather than answered.
Order status, returns and exchanges, delivery questions, and product help read straight from your catalogue.
Admissions and enrolment questions, deadlines, fees and timetables — plus the spike that arrives on results day.
Billing queries, plan changes, and first-line troubleshooting that ends in a ticket with the diagnostics already attached.
Policy, leave and benefits questions answered from the handbook, plus screening and onboarding steps that route to a person.
Subscription and billing questions, playback problems, and recommendations drawn from your own catalogue.
Orders and reservations, allergen and menu questions answered from real menu data, and delivery status.
Class booking and cancellation, membership and billing questions, and progress check-ins — with health advice deliberately out of scope.
Booking changes, baggage and check-in questions, and the surge of enquiries the moment something is delayed.
Timelines, cost, security, and how to stop a public bot saying something you'd have to apologise for. Ask us directly.
Start with your ticket queue
Share a sample of your tickets or chat logs, plus the content you'd want an assistant to answer from. We'll tell you how much of it is safely automatable today, where your content falls short, and what a first release would take.
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The problem, who it's for, and where you are now. That's enough for us to come back with something useful.
hello@aryavos.com
HQ
Noida, India & New York, USA
What happens after you send it
A developer reads it, not an auto-responder. Expect questions about scope and constraints first, then a suggested way to start.
